Update README.md

README.md

@@ -1,6 +1,13 @@
 ---
 base_model: openai/whisper-large-v3-turbo
 library_name: peft
+license: mit
+language:
+- ar
+metrics:
+- cer
+- wer
+pipeline_tag: automatic-speech-recognition
 ---
 # 🗣️ Whisper Large v3 Turbo – Moroccan Darija (LoRA Fine-tuned)
 
@@ -84,15 +91,12 @@ print(output["text"])
 - **Optimizer:** AdamW  
 - **Seed:** 42  
 - **Training Time:** ~4.1 hours on 1 × H100 80 GB
-- **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `out_proj`, `fc1`, `fc2` |
+- **Target Modules:** `q_proj`, `k_proj`, `v_proj`, `out_proj`, `fc1`, `fc2`
 - **Rank (`r`):** 16
-- **Alpha (`lora_alpha`):** | 32 |
+- **Alpha (`lora_alpha`):** 32
 
 ### 🎧 Dataset
 - **Data Source:** Private Moroccan Darija speech corpus (to be released soon).  
-- **Segmentation:** All audio split into ≤30 s chunks.
-
-> The full dataset used to train this model will be open-sourced soon.
 
 ---
 
@@ -107,5 +111,4 @@ print(output["text"])
 Evaluation was performed on a held-out subset from the same data distribution.
 The model achieves a low CER but a relatively higher WER compared to other languages. This difference is mainly due to the absence of a standardized writing system for Darija in Morocco — many words can be spelled in several valid ways. This variability also reflects a limitation of the dataset used for fine-tuning and highlights the need to establish a consistent orthographic standard for Darija before large-scale data collection efforts.
 
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-
+---